POSTER: Enhancements of Simulated Science Laboratory Assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of digitally simulated environments to assess science knowledge and skills has become popular in recent years (Bennett, Persky, Weiss, & Jenkins, 2007; PhET, 2014). Digital environments are often superior to face-to-face environments in which traditional science laboratories are usually conducted. Traditional science laboratories have been criticized for providing a recipe of pre-determined linear steps for student to follow. In contrast, simulated science laboratories encourage higher-order ideas such as scientific inquiry by allowing students to explore the laboratory (e.g., trying different procedures and making errors; Ma & Nickerson, 2006; Sahin, 2006). Although these simulations mimic and surpass many traditional laboratories in terms of bringing real-world science into the classroom, many of them tend to omit the use of a pre-laboratory activity (which are often used in traditional laboratories to cognitively prepare students for the experiment; Sahin, 2006; PheT, 2014). These digital laboratories encourage students to attempt multiple procedures while solving one problem, while traditional laboratories do not allow for much deviation from the linear steps (Bennett et al., 2007; Ma & Nickerson, 2006). These multiple trials are not errors, but an essential part of the learning process, because they may inform future runs (Author, Author, & Author, Year). Hence, students are encouraged to make learning errors throughout the simulation. This study investigated whether two treatments – pre-laboratory activity and learning error intervention – enhanced students’ performance on a digitally simulated science laboratory. The results indicated students who received the learning error intervention significantly outperformed students who did not have the intervention, F (1, 244)=8.084, p <0.01, partial eta squared=0.032. This finding is important because it indicates the need for supplementary instruction when using simulated science laboratory assessment tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it